A Domino subarray layout design method based on subarray footprint

Abstract

The present invention relates to the technical field of phased array antennas, and in particular, to a Domino subarray layout design method based on subarray footprints, comprising: arranging subarray footprints in an array aperture based on an invasive weed optimization algorithm; sequentially arranging multiple Domino subarrays into the array aperture based on the distribution of subarray footprints in the array aperture and a preset subarray arrangement rule, thereby completing the optimization design of the Domino subarray layout. The present invention discloses a Domino subarray layout design method based on subarray footprints, wherein the subarray footprint determines the Domino subarray layout, and the Domino subarray layout is optimized by an invasive weed optimization algorithm. This method is simple and easy to implement, does not require ingenious mathematical modeling, and can find a Domino subarray layout with the best beam scanning directivity coefficient performance.

Description

Technical Field

[0001] The present invention relates to the technical field of phased array antennas, and in particular to a Domino subarray layout design method based on subarray footprints. Background Art

[0002] Large-scale phased array antennas have high gain characteristics and flexible beam scanning capabilities, and have been widely used in various communications and radar systems. In traditional phased array antennas, each antenna unit is equipped with a set of transceiver components to adjust the feed power and phase of this antenna unit. However, due to the immaturity of the design technology and manufacturing process of existing transceiver components, the transceiver components in the high-frequency band are large in size and expensive, which leads to the phased array antennas working in higher frequency bands facing the problems of insufficient space and high cost for transceiver component placement. Irregular subarray technology can significantly reduce the number and density of transceiver components by placing the transceiver components at the subarray level, allowing multiple antenna units to share a set of transceiver components, thereby effectively solving the above problems.

[0003] When using irregular subarray technology, the layout of the subarray has a significant impact on the performance of the phased array. In recent years, the mainstream method for designing subarray layout is to establish a mixed integer programming model based on performance index requirements, thereby selecting several subarrays from all candidate subarrays in the array aperture to form a complete subarray layout. Although this type of method has achieved good results, the modeling of the mixed integer programming model is very skill-intensive and the solution is very complex. Summary of the invention

[0004] The purpose of the present invention is to provide a Domino subarray layout design method based on subarray footprint, so as to improve the mathematical modeling and complex solution problems faced by the above-mentioned mainstream methods for designing subarray layout.

[0005] In order to achieve the above objectives, the present application provides the following technical solutions:

[0006] The present application embodiment provides a Domino subarray layout design method based on subarray footprint, the method comprising: steps S 100. Arrange the subarray footprints in the array aperture based on the invasive weed optimization algorithm; Steps S 200. Based on the distribution of subarray footprints in the array aperture and the preset subarray arrangement rules, multiple Domino subarrays are sequentially arranged in the array aperture, thereby completing the optimization design of the Domino subarray layout.

[0007] Optionally, the arranging sub-array footprints in the array aperture based on the invasive weed optimization algorithm includes:

[0008] Step S110, randomly initializing a weed population, wherein each weed in the population is represented by a vector consisting of a set of horizontal and vertical coordinates of a subarray footprint, and the positions of a number of antenna units are randomly selected as the positions of the subarray footprint, and the number of subarray footprints is determined by the array scale and the subarray size;

[0009] Step S120, determining the Domino sub-array layout corresponding to each weed according to the sub-array arrangement rule;

[0010] Step S130, weed fitness evaluation. The weed fitness is used to measure the directivity coefficient performance of arrays with different Domino subarray layouts when performing beam scanning. The fitness value is equal to the array φ =0° and φ = The minimum directivity coefficient when the beam is scanned in two azimuth planes at 90°, φ is the angle between the scanning angle and the positive x-axis in the azimuth plane;

[0011] Step S140, the weed population propagates and spreads based on the probability mutation mechanism. When the population propagates and spreads, each weed in the current weed population will serve as a parent weed and produce a number of child weeds. The higher the fitness value of the parent weed, the more child weeds will be produced, and vice versa. Next, each child weed will first copy the vector of the parent weed when it is produced, and then perform probability mutation, that is, each element in the vector will be added or subtracted by 1 with a preset probability, wherein the mutation probability decreases with the increase of the evolutionary generation number, so as to realize the gradual transition from global search to local search, and ensure the rate of convergence to the optimal value. The produced child weeds and the parent weeds together constitute a new population;

[0012] Step S150, determining the Domino sub-array layout corresponding to the weeds, wherein for each weed in the new population, the corresponding Domino sub-array layout is determined according to the sub-array arrangement rule;

[0013] Step S160, weed fitness evaluation, including recalculating the fitness value of each weed in the new population;

[0014] Step S170, competitive elimination of weed populations, including determining whether the current weed population size exceeds the maximum population size after the weed populations reproduce as described above, and if so, eliminating weeds with the lowest fitness values ​​in sequence until the population size is equal to the maximum population size;

[0015] Step S180, check whether the evolutionary generation has reached the maximum generation. If so, output the Domino subarray layout corresponding to the weed with the highest fitness value in the current population and the algorithm stops; otherwise, go back to step S140 and continue to execute subsequent steps.

[0016] Optionally, the sub-array arrangement rule is to number the antenna units before arranging the Domino sub-array, and the numbering order is from left to right and from bottom to top; among all antenna units not covered by the Domino sub-array, the unit with the smallest number will be covered by the Domino sub-array first;

[0017] For each antenna unit to be covered by a subarray, there will be one or two Domino subarrays that can be arranged without causing subarray overlap. In the case of two Domino subarray arrangements, based on the distance from the center position of each Domino subarray to the footprint of each subarray, the Domino subarray with the shortest distance is selected for layout. After the Domino subarray is selected, it is arranged in the aperture, and the subarray footprint is deleted after it is selected.

[0018] If the distances between the two Domino sub-arrays and the nearest sub-array footprint are the same, the Domino sub-array with the smaller sum of the unit numbers corresponding to the Domino sub-arrays is selected for arrangement.

[0019] The beneficial effects of the present invention are:

[0020] The present invention discloses a Domino subarray layout design method based on subarray footprint, wherein the subarray footprint determines the Domino subarray layout and is optimized by an invasive weed optimization algorithm. The method is simple and easy to implement, does not require ingenious mathematical modeling, and can find a Domino subarray layout with the best beam scanning directivity coefficient performance.

[0021] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of arranging Domino sub-arrays according to rules based on the sub-array footprint positions.

[0024] Figure 2 Flowchart for optimizing subarray footprints using the invasive weed optimization algorithm.

[0025] Figure 3 The diagram is a schematic diagram of the optimal Domino subarray layout obtained by the present invention.

[0026] Figure 4 After adopting the optimal Domino subarray layout for the array φ =0° when performing beam scanning in the azimuth plane.

[0027] Figure 5 After adopting the optimal Domino subarray layout for the array φ = 90° beam scanning performance DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] It should be noted that similar reference numerals or letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0030] Embodiment 1:

[0031] like Figure 1 As shown, this embodiment provides a Domino sub-array layout design method based on sub-array footprint, and the method includes step S100 and step S200.

[0032] Step S100, arranging sub-array footprints in the array aperture based on the invasive weed optimization algorithm;

[0033] Step S200: based on the distribution of the sub-array footprints in the array aperture and the preset sub-array arrangement rules, multiple Domino sub-arrays are sequentially arranged in the array aperture, thereby completing the optimization design of the Domino sub-array layout.

[0034] The specific implementation method of arranging the sub-array footprints in the array aperture based on the invasive weed optimization algorithm described in step S100 is:

[0035] Step S110, randomly initializing a weed population, wherein each weed in the population is represented by a vector consisting of a set of horizontal and vertical coordinates of a subarray footprint, and the positions of a number of antenna units are randomly selected as the positions of the subarray footprint, and the number of subarray footprints is determined by the array scale and the subarray size;

[0036] Step S120, determining the Domino sub-array layout corresponding to each weed according to the sub-array arrangement rule;

[0037] Step S130, weed fitness evaluation. The weed fitness is used to measure the directivity coefficient performance of arrays with different Domino subarray layouts when performing beam scanning. The fitness value is equal to the array φ =0° and φ = The minimum directivity coefficient when the beam is scanned in two azimuth planes at 90°, φ is the angle between the scanning angle and the positive x-axis in the azimuth plane;

[0038] Step S140, the weed population propagates and spreads based on the probability mutation mechanism. When the population propagates and spreads, each weed in the current weed population will serve as a parent weed and produce a number of daughter weeds. The higher the fitness value of the parent weed, the more daughter weeds will be produced, and vice versa. Secondly, each daughter weed will first copy the vector of the parent weed when it is produced, and then perform probability mutation, that is, each element in the vector will be added or subtracted by 1 with a preset probability, wherein the mutation probability decreases with the increase of the evolutionary generation number, so as to realize the gradual transition from global search to local search, and ensure that it converges to the optimal value at a faster rate. The produced daughter weeds and the parent weeds together constitute a new population;

[0039] Step S150, determining the Domino sub-array layout corresponding to the weeds, wherein for each weed in the new population, the corresponding Domino sub-array layout is determined according to the sub-array arrangement rule;

[0040] Step S160, weed fitness evaluation, including recalculating the fitness value of each weed in the new population;

[0041] step S 170. Competitive elimination of weed populations, including, after the weed populations have reproduced as described above, determining whether the current weed population size exceeds the maximum population size, and if so, eliminating weeds with the lowest fitness values ​​in turn until the population size is equal to the maximum population size;

[0042] Step S180, check whether the evolutionary generation has reached the maximum generation. If so, output the Domino subarray layout corresponding to the weed with the highest fitness value in the current population and the algorithm stops; otherwise, go back to step S140 and continue to execute subsequent steps.

[0043] The sub-array arrangement rule is to number the antenna units before arranging the Domino sub-array, and the numbering order is from left to right and from bottom to top; among all antenna units not covered by the Domino sub-array, the unit with the smallest number will be covered by the Domino sub-array first;

[0044] For each antenna unit to be covered by a subarray, there will be one or two Domino subarrays that can be arranged without causing subarray overlap. In the case of two Domino subarray arrangements, based on the distance from the center position of each Domino subarray to the footprint of each subarray, the Domino subarray with the shortest distance is selected for layout. After the Domino subarray is selected, it is arranged in the aperture, and the subarray footprint is deleted after it is selected.

[0045] If the distances between the two Domino sub-arrays and the nearest sub-array footprint are the same, the Domino sub-array with the smaller sum of the unit numbers corresponding to the Domino sub-arrays is selected for arrangement.

[0046] The present embodiment discloses a Domino subarray layout design method based on subarray footprint, in which the subarray footprint determines the Domino subarray layout and is optimized by an invasive weed optimization algorithm. This method is simple and easy to implement, does not require ingenious mathematical modeling, and can find a Domino subarray layout with the best beam scanning directivity coefficient performance.

[0047] Embodiment 2:

[0048] The subarray footprints are distributed in the array aperture, and correspond one-to-one with the Domino subarrays to be arranged based on a set of subarray arrangement rules, thus determining the Domino subarray layout. By optimizing the subarray footprint position, the optimal design of the Domino subarray layout can be achieved. The optimal position of the subarray footprint is searched by the invasive weed optimization algorithm.

[0049] For a given set of subarray footprints, Domino subarrays are arranged based on the following rules: before arranging the Domino subarrays, the antenna units are numbered in order from left to right and from bottom to top; among all antenna units not covered by the subarrays, the unit with the smallest number will be covered by the subarrays first; for each antenna unit to be covered by the subarrays, one or two Domino subarrays can be arranged without causing subarray overlap, so the distance from the position center of each Domino subarray to each subarray footprint needs to be calculated, and a pair of Domino subarrays and subarray footprints with the shortest distance will be selected, where the subarray is arranged in the aperture after being selected, and the subarray footprint is deleted after being selected.

[0050] The steps for searching the optimal subarray footprint location by the invasive weed optimization algorithm are as follows:

[0051] Step 1: Randomly initialize the weed population. Each weed in the population is represented by a vector consisting of the horizontal and vertical coordinates of a set of subarray footprints, and the positions of several antenna units are randomly selected as the positions of the subarray footprints. The number of subarray footprints is determined by the array scale and subarray size.

[0052] Step 2: Determine the Domino sub-array layout corresponding to the weeds. Determine the Domino sub-array layout corresponding to each weed according to the above sub-array arrangement rules.

[0053] Step 3: Weed fitness evaluation. The weed fitness is used to measure the directivity coefficient performance of arrays with different Domino subarray layouts during beam scanning. The fitness value is equal to the array in φ =0° and φ =The minimum directivity coefficient when beam scanning is performed on two azimuth planes of 90°.

[0054] Step 4: Propagation and diffusion of weed population based on probability mutation mechanism. When the population is propagating and spreading, each weed in the current weed population will serve as a parent weed and produce several daughter weeds. The higher the fitness value of the parent weed, the more daughter weeds will be produced, and vice versa. When each daughter weed is produced, it will first copy the vector of the parent weed, and then undergo probability mutation, that is, each element in the vector will be added or subtracted by 1 with a preset probability. The mutation probability is large in the early stage of evolution and decreases with the increase of the number of evolutionary generations, so as to achieve a gradual transition from global search to local search, thereby ensuring convergence to the optimal value at a faster rate. The mutated daughter weeds need to be post-processed to ensure that the subarray footprint corresponding to the daughter weed is still within the array aperture. The generated daughter weeds and the parent weeds together constitute a new population.

[0055] Step 5: Determine the Domino sub-array layout corresponding to the weeds. For each weed in the new population, determine its corresponding Domino sub-array layout according to the above sub-array arrangement rules.

[0056] Step 6: Weed fitness evaluation: For each weed in the new population, calculate its fitness value again.

[0057] Step 7: Competitive elimination of weed populations. After the weed populations have reproduced as described above, determine whether the current weed population size exceeds the maximum population size. If so, weeds with the lowest fitness values ​​are eliminated in turn until the population size is equal to the maximum population size.

[0058] Step 8: Check whether the evolutionary generation has reached the maximum generation. If it has, output the Domino subarray layout corresponding to the weed with the highest fitness value in the current population and the algorithm stops; otherwise, go back to step 4 and continue to execute the algorithm.

[0059] The beneficial effect of the present invention is that the present invention provides a simple and easy Domino subarray layout design method, which does not require clever mathematical modeling and can find the Domino subarray layout with the best beam scanning directivity coefficient performance.

[0060] Embodiment 3:

[0061] This embodiment will use a specific case to illustrate the principle of the above method. When the array size considered is 8×8, the schematic diagram of arranging sub-arrays according to the rules based on the sub-array footprint is as follows: Figure 1 shown. Figure 1 There are 64 antenna elements in total, numbered from left to right and from bottom to top. At most 32 Domino subarrays are required to cover the array aperture without overlap, so there are 32 subarray footprints. Figure 1 The subarray footprint positions in are given in advance and represented by circles. Now the Domino subarrays are arranged according to the subarray arrangement rules. The Domino subarrays are represented by rectangular boxes. Figure 1 In the array on the left side of the first row, among all the antenna units not covered by the subarray, the unit with the smallest number is the first antenna unit, so it will be covered first. At this time, the first antenna unit can form a horizontal Domino subarray with the second antenna unit, or a vertical Domino subarray with the third antenna unit. Since the pair of Domino subarrays and subarray footprints with the shortest distance are the horizontal Domino subarray and the subarray footprint at the position of the second antenna unit, the horizontal Domino subarray is arranged in the array aperture, and the subarray footprint at the position of the second antenna unit is deleted, as shown in FIG. Figure 1After arranging 14 Domino arrays according to the rules, the Domino array layout and the remaining array footprints are shown in Figure 1 As shown in the array on the left side of the second row in . At this time, among all the antenna units not covered by the subarray, the unit with the smallest number is the 27th antenna unit, so it will be covered first. The 27th antenna unit can form a horizontal Domino subarray with the 28th antenna unit, or a vertical Domino subarray with the 35th antenna unit. Since the pair of Domino subarrays and subarray footprints with the shortest distance are the horizontal Domino subarray and the subarray footprint at the 36th antenna unit position, the horizontal Domino subarray is arranged in the array aperture, and the subarray footprint at the 36th antenna unit position is deleted, as shown in FIG. Figure 1 Based on the same rule, we can continue to arrange Domino sub-arrays to cover the remaining cells until we can no longer place Domino sub-arrays that do not overlap with other sub-arrays, as shown in the array on the right side of the second row in the figure. Figure 1 shown.

[0062] The flowchart of optimizing the subarray footprint by using the invasive weed optimization algorithm is as follows: Figure 2 As shown. Since the array consists of 64 antenna elements, the array aperture needs to be covered by at most 32 Domino subarrays without overlap. Therefore, a weed in this algorithm is a vector with a length of 64, and the first 32 elements and the last 32 elements represent the horizontal and vertical coordinates of the 32 subarray footprints respectively. The subarray footprint can only be located at the position of the antenna element, that is, the horizontal and vertical coordinates of the subarray footprint can only be integers in [1,8]. The steps to optimize the subarray footprint by the invasive weed optimization algorithm are as follows:

[0063] Step 1: Randomly initialize the weed population, and the initial population size is set to 5 weeds. Each weed in the population is obtained by randomly selecting 32 positions from the 64 antenna unit positions as the position of the subarray footprint and forming their horizontal and vertical coordinates into a vector.

[0064] Step 2: Determine the Domino sub-array layout corresponding to the weeds. After the initial population is formed, the Domino sub-array layout corresponding to each weed can be determined according to the above sub-array arrangement rules.

[0065] Step 3: Weed fitness evaluation. The weed fitness is used to measure the directivity coefficient performance of arrays with different Domino subarray layouts during beam scanning. The fitness value is equal to the array in φ =0° and φ =90° is the minimum directivity coefficient when the beam is scanned in two azimuth planes. This embodiment considers scanning ±45° in two azimuth planes, and considers the unit direction is cos ( θ ) 0.5 , the Domino subarray feeding amplitude is 1, and the feeding phase is based on the scanning angle ( θ , φ ) and the subarray position center ( x c , y c ) by -2 π / λ ×( x c sin ( θ ) cos ( φ ) + y c sin ( θ ) sin ( φ )) calculation. Since the directivity coefficient of the array scanning beam decreases with the increase of the scanning angle, the minimum directivity coefficient appears at the maximum scanning angle of the two azimuth planes, that is, the fitness value = min { D (θ=45°,φ=0°) , D (θ=－45°,φ=0°) , D (θ=45°,φ=90°) , D (θ=－45°,φ=90°)}, here D (θ,φ) Indicates scanning to ( θ , φ ) when the directivity coefficient.

[0066] Step 4: Propagation and diffusion of weed population based on probability mutation mechanism. When the population is propagating and spreading, each weed in the current weed population will serve as a parent weed and produce several offspring weeds. The higher the fitness value of the parent weed, the more offspring weeds it produces, and vice versa. The number of offspring weeds produced by the parent weed can be selected in [1, 5] according to its fitness value. When each offspring weed is produced, it will first copy the vector of the parent weed, and then undergo probability mutation, that is, each element in the vector will be added or subtracted by 1 with a preset probability. The mutation probability is relatively large at the beginning of evolution, which is 10 / 64. As the number of evolutionary generations increases, the mutation probability gradually decreases to 1 / 64, so as to achieve a gradual transition from global search to local search, thereby ensuring that it converges to the optimal value at a faster rate. In order to ensure that the subarray footprint corresponding to the mutated offspring weed is still within the array aperture, the horizontal and vertical coordinates of the subarray footprint corresponding to the mutated offspring weed must still be integers in [1, 8]. Therefore, among all the elements of the mutated offspring weeds, those less than 1 will be reassigned to 1, and those greater than 8 will be reassigned to 8. The resulting offspring weeds and the parent weeds together constitute a new population.

[0067] Step 5: Determine the Domino sub-array layout corresponding to the weeds. For each weed in the new population, determine its corresponding Domino sub-array layout according to the above sub-array arrangement rules.

[0068] Step 6: Weed fitness evaluation: For each weed in the new population, calculate its fitness value again.

[0069] Step 7: Competitive elimination of weed populations. After the weed populations have reproduced as described above, it is determined whether the current weed population size exceeds the maximum population size. If so, weeds with the lowest fitness values ​​are eliminated in turn until the population size is equal to the maximum population size. In this embodiment, the maximum population size is set to 20.

[0070] Step 8: Check whether the evolutionary generation has reached the maximum generation. If it has, output the Domino subarray layout corresponding to the weed with the highest fitness value in the current population and the algorithm stops; otherwise, go back to step 4 and continue to execute the algorithm. In this embodiment, the maximum iteration generation is set to 200.

[0071] Finally, the optimal Domino subarray layout obtained by the present invention is as follows: Figure 3 As shown, the array adopts this sub-array layout. φ =0° and φ =90° The directivity coefficient performance when the beam scans at ±45° on two azimuth planes is as follows Figure 4 and Figure 5 As shown. Figure 4 and Figure 5By comparison, it can be seen that the scanning performance on the two azimuth planes is basically the same, and the minimum directivity coefficient is 19. dBi .

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A Domino subarray layout design method based on subarray footprint, characterized in that: The method comprises: Step S100, arranging sub-array footprints in the array aperture based on the invasive weed optimization algorithm; Step S200: based on the distribution of the sub-array footprints in the array aperture and the preset sub-array arrangement rules, multiple Domino sub-arrays are sequentially arranged in the array aperture, thereby completing the optimization design of the Domino sub-array layout; Secondly, the sub-array footprints are arranged in the array aperture based on the invasive weed optimization algorithm, including: Step S110, randomly initializing a weed population, wherein each weed in the population is represented by a vector consisting of a set of horizontal and vertical coordinates of a subarray footprint, and the positions of a number of antenna units are randomly selected as the positions of the subarray footprint, and the number of subarray footprints is determined by the array scale and the subarray size; Step S120, determining the Domino sub-array layout corresponding to each weed according to the sub-array arrangement rule; Step S130, weed fitness evaluation. The weed fitness is used to measure the directivity coefficient performance of arrays with different Domino subarray layouts when performing beam scanning. The fitness value is equal to the array φ =0° and φ = The minimum directivity coefficient when the beam is scanned in two azimuth planes at 90°, φ is the angle between the scanning angle and the positive x-axis in the azimuth plane; Step S140, the weed population propagates and spreads based on the probability mutation mechanism. When the population propagates and spreads, each weed in the current weed population will serve as a parent weed and produce a number of child weeds. The higher the fitness value of the parent weed, the more child weeds will be produced, and vice versa. Next, each child weed will first copy the vector of the parent weed when it is produced, and then perform probability mutation, that is, each element in the vector will be added or subtracted by 1 with a preset probability, wherein the mutation probability decreases with the increase of the evolutionary generation number, so as to realize the gradual transition from global search to local search, and ensure the rate of convergence to the optimal value. The produced child weeds and the parent weeds together constitute a new population; Step S150, determining the Domino sub-array layout corresponding to the weeds, wherein for each weed in the new population, the corresponding Domino sub-array layout is determined according to the sub-array arrangement rule; Step S160, weed fitness evaluation, including recalculating the fitness value of each weed in the new population; Step S170, competitive elimination of weed populations, including determining whether the current weed population size exceeds the maximum population size after the weed populations reproduce as described above, and if so, eliminating weeds with the lowest fitness values ​​in sequence until the population size is equal to the maximum population size; Step S180, check whether the evolutionary generation has reached the maximum generation. If so, output the Domino subarray layout corresponding to the weed with the highest fitness value in the current population and the algorithm stops; otherwise, go back to step S140 and continue to execute subsequent steps.

2. The Domino subarray layout design method based on subarray footprint according to claim 1, characterized in that: The sub-array arrangement rule is to number the antenna units before arranging the Domino sub-array, and the numbering order is from left to right and from bottom to top; among all antenna units not covered by the Domino sub-array, the unit with the smallest number will be covered by the Domino sub-array first; For each antenna unit to be covered by a subarray, there will be one or two Domino subarrays that can be arranged without causing subarray overlap. In the case of two Domino subarray arrangements, based on the distance from the center position of each Domino subarray to the footprint of each subarray, the Domino subarray with the shortest distance is selected for layout. After the Domino subarray is selected, it is arranged in the aperture, and the subarray footprint is deleted after it is selected. If the distances between the two Domino sub-arrays and the nearest sub-array footprint are the same, the Domino sub-array with the smaller sum of the unit numbers corresponding to the Domino sub-arrays is selected for arrangement.

Images